Int. J. Communications, Network and System Sciences, 2009, 7, 608-618
doi:10.4236/ijcns.2009.27068 Published Online October 2009 (
Copyright © 2009 SciRes. IJCNS
A New Fairness-Oriented Packet Scheduling Scheme
with Reduced Channel Feedback for
OFDMA Packet Radio Systems
Stanislav NONCHEV, Mikko VALKAMA
Department of Communications Engineering, Tampere University of Technology, Tampere, Finland
Email: {stanislav.nonchev, mikko.e.valkama}
Received June 26, 2009; revised August 13, 2009; accepted September 26, 2009
In this paper, we propose a flexible and fairness-oriented packet scheduling approach for 3GPP UTRAN
long term evolution (LTE) type packet radio systems, building on the ordinary proportional fair (PF) sched-
uling principle and channel quality indicator (CQI) feedback. Special emphasis is also put on practical feed-
back reporting mechanisms, including the effects of mobile measurement and estimation errors, reporting
delays, and CQI quantization and compression. The performance of the overall scheduling and feedback re-
porting process is investigated in details, in terms of cell throughput, coverage and resource allocation fair-
ness, by using extensive quasi-static cellular system simulations in practical OFDMA system environment
with frequency reuse of 1. The performance simulations show that by using the proposed modified PF ap-
proach, significant coverage improvements in the order of 50% can be obtained at the expense of only
10-15% throughput loss, for all reduced feedback reporting schemes. This reflects highly improved fairness
in the radio resource management (RRM) compared to other existing schedulers, without essentially com-
promising the cell capacity. Furthermore, we demonstrate the improved functionality increase in radio re-
source management for UE’s utilizing multi-antenna diversity receivers.
Keywords: Radio Resource Management, Packet Scheduling, Proportional-Fair, Channel Quality Feedback,
Throughput, Fairness
1. Introduction
Development of new radio interface technologies for
beyond 3G cellular radio systems with support to high
data rates, low latency and packet-optimised radio access
has led to the use of OFDM/OFDMA. One good exam-
ple of such developments is e.g. the UTRAN long term
evolution (LTE), being currently standardized by 3GPP
[1–3]. In general, performance improvements over the
existing radio systems are basically obtained through
proper deployment of fast link adaptation and new
packet scheduling algorithms, exploiting the available
multi-user diversity in both time and frequency domains
[4–6]. On the other hand, achieving such performance
improvements typically requires relatively accurate
channel state feedback in terms of CQI reports from mo-
bile stations (MS) to the base station (BS) [6–12]. As a
practical example, each mobile station can measure the
effective signal-to-interference-plus-noise-ratio (SINR),
per active subcarrier or block of subcarriers, and send
back the obtained channel state to the base station for
downlink radio resource management. This, in turn, can
easily lead to considerable control signalling overhead if
not designed and implemented properly. Thus in general,
the amount of the feedback information needs to be lim-
ited and is also subject to different errors and delays,
affecting the overall system-level performance. Another
important aspect in scheduling and resource allocation
process is fairness, implying that also users with less
favourable channel conditions should anyway be given
some reasonable access to the radio spectrum [4–6,13–
18]. This is especially important in serving users at, e.g.,
cell edges in cellular networks.
In this paper, we address the packet scheduling and
channel state reporting tasks in OFDMA-based cellular
packet radio systems. Stemming from ordinary propor-
tional fair (PF) scheduling principle, a modified PF
scheduler is first proposed having great flexibility to tune
the exact scheduling characteristics in terms of capacity,
coverage and fairness. More specifically, the proposed
scheduler can offer greatly improved fairness among the
users in a cell, measured in terms of coverage and other
established fairness measures, like Jain’s index [19],
without essentially compromising the overall cell capac-
ity. This is verified using extensive quasi-static cellular
system simulations, conforming to the current LTE
downlink specifications [1–3]. In the performance stud-
ies, different realistic CQI reporting schemes are also
addressed and incorporated in the system simulations.
In general, the research on novel packet scheduling
algorithms and channel state reporting schemes has been
very active in the recent years, see e.g. [8,10,11,13–18]
and the references therein. Using [13–17] as starting
points for LTE type packet radio systems, it has been
reported that frequency domain packet scheduling (FDPS)
algorithms are always a compromise between the overall
cell throughput and resource fairness among users. Here
we propose a modified proportional fair algorithm,
which in general offers an attractive balance between cell
throughput, coverage and user fairness. Compared to
plain frequency domain scheduling, we extend the stud-
ies by deploying both time domain and frequency do-
main scheduling steps, together with proper metrics, that
as a whole can more efficiently utilise the provided yet
limited feedback information from all the user equip-
ments (UEs). Furthermore, we apply different realistic
CQI reporting schemes to thoroughly investigate the lim-
its of achieved performance gains from enhanced sched-
uling. The cellular system model used for the perform-
ance evaluations is fully conforming to the 3GPP evalua-
tion criteria [1–3]. The overall outcomes are measured in
terms of average cell throughput, coverage and fairness
The rest of the paper is organised as follows: Section 2
reviews the reference proportional fair scheduler and
proposes then a modified PF scheduling scheme. Section
3, in turn, addresses different feedback reporting
schemes in the scheduling context. Section 4 presents
then the overall system model and simulation assump-
tions, and the simulation results and analysis are pre-
sented in Section 5. Finally, the conclusions are drawn in
Section 6.
2. Scheduling Process
2.1. General Scheduling and Link Adaptation
In general, the task of a packet scheduler (PS) is to select
the most suitable users to access the available radio
spectrum at any given time window, in order to optimize
the system performance in terms of 1) throughput, 2)
resource fairness, and/or 3) delay [4–6]. Joint optimiza-
tion of all the above features is generally known very
difficult. In fast packet scheduling, new scheduling deci-
sions are basically taken in each transmission time inter-
val (TTI), which in LTE is 1ms.
To efficiently utilize the limited radio resources, the
scheduler should consider the current state of the channel
when selecting the user to be scheduled, by utilizing e.g.
the ACK/NACK signalling information and CQI reports
[4–6,8,10,11,14]. Depending on the selected CQI report-
ing scheme, the accuracy and resolution of the channel
quality information can easily differ considerably. In
OFDMA based radio systems, like LTE, the CQI infor-
mation is not necessarily available for all the individual
subcarriers but more likely for certain groups of subcar-
riers only [12,20–22]. In general, the channel state in-
formation is also used by link adaptation (LA) mecha-
nisms to select proper modulation and coding scheme
(MCS) for each scheduled mobile, and thereon to ensure
that the individual link qualities conform to the corre-
sponding target settings. This is typically measured in
terms of block error rate (BLER) for the first transmis-
sion. Hybrid ARQ (HARQ) mechanisms are then com-
monly used to provide the necessary buffer information
and transmission format for pending retransmissions
[4–6,16]. A principal block-diagram of the overall RRM
flow is given in Figure 1.
As a practical example of the available spectral re-
sources, in the 10 MHz system bandwidth case of LTE
[1–3], there are 50 physical resource blocks (PRB’s or
sub-bands), each consisting of 12 sub-carriers with sub-
carrier spacing of 15 kHz. This sets the basic resolution
in frequency domain (FD) UE multiplexing (scheduling),
i.e., the allocated individual UE bandwidths are multiples
of the PRB bandwidth.
2.2. Ordinary Proportional Fair (PF) Scheduler
The well-known proportional fair scheduler [13,16]
works in two steps: 1) time domain (TD) PF step and 2)
frequency domain (FD) PF step. Such simplified sche-
Figure 1. Principal RRM block diagram.
Copyright © 2009 SciRes. IJCNS
duling principle is beneficial from the complexity point
of view, since the FD step considers a reduced number of
UEs for frequency multiplexing in each TTI [17]. Thus
in the first part, inside each TTI n, all the UE’s are
ranked according to the following priority metric
() ()
In above, the UE index i = 1, 2, …, ITOT, Ri(n) denotes
the estimated throughput to the UE i over the full band-
width (provided by link adaptation unit) [13,16], and Ti(n)
in turn is the corresponding average delivered throughput
to the UE i during the recent past and can be obtained,
e.g., recursively by
()1( 1)( 1)
TnTn Rn
 
 (2)
In (2), tc controls the averaging window length over
which the average delivered throughput is calculated and
R'i(n-1) denotes the actually realized throughput to the
UE i at the previous TTI.
In the next step, out of this ranked list of UE’s, the
first IBUFF (< ITOT) UE’s with highest priority metric are
picked to the actual frequency domain multiplexing or
scheduling stage. In the following, this subset is called
scheduling candidate set (SCS), and is denoted by (n).
Then, for each physical resource block k = 1, 2, …, KTOT,
and for each i belonging to the SCS, the following final
scheduling metric of the form
() ()
ik i
is evaluated where now Ri,k(n) denotes the estimated
throughput to the UE i for the k-th PRB (provided by LA
unit again), and Ti(n) is again the corresponding average
throughput delivered to the UE i during the recent past
given in (2). Finally, the access to each PRB resource is
granted for the particular user with the highest metric for
the corresponding PRB.
2.3. Proposed Modified PF (MPF) Scheduler
In order to obtain a scheduler with yet increased fairness
in the resource allocation, we proceed as follows. First
the time domain priority metric is modified as
()() ()
where CQIi(n) denotes the full bandwidth channel qual-
ity report for UE i at TTI n and Ti(n) is as defined in (2).
Ttot(n), in turn, denotes the averaged throughput over the
past and over the scheduled users and can be calculated
()1( 1)
11 (1
tot tot
Tn Tn
tI  
 
In (5), R'i(n-1) denotes the actual delivered throughput
for UE i at the previous TTI.
Similar to the ordinary PF scheduler described in
Subsection 2.2, this modified metric in (4) is used to rank
the UE’s inside each TTI, and the IBUFF (< ITOT) UE’s
with highest priority metric form a SCS. (n) for the
actual frequency domain resource allocation. Since esti-
mated throughput in the link adaptation stage is based on
reported CQI values, we assume that the substitution in
(4) has the same weight in priority calculation. For map-
ping the users of the SCS into PRB’s, the following
modified frequency domain metric is then proposed:
() ()
() ()
fd i
ik avg tot
 (6)
Here s1 and s2 are adjustable parameters, and CQIi,k(n)
is the channel quality report of user i for sub-band k at
TTI n while CQIi
avg(n) is the corresponding average CQI
over the past and over the sub-bands, and can be calcu-
lated using
()1( 1)
11 ()
avg avg
The access to each PRB resource is then granted for
the particular user with the highest metric in (6) for the
corresponding PRB.
Considering the re-transmissions, re-transmitting users
are simply considered as additional users in the time do-
main scheduling part (step 1), and if qualified to the fre-
quency domain SCS, the re-transmission users are given
an additional priority to reserve exactly the same sub-
bands used for the corresponding original transmissions.
Even though this does not take the exact sub-band condi-
tion into account at re-transmission stage, the practical
implementation is simplified, in terms of control signal-
ling, and re-transmissions anyway always benefit from
the HARQ combining gain [6].
Intuitively, the proposed scheduling metrics in (4) and
(6) are composed of two elements, affecting the overall
scheduling decisions. The first dimension measures the
relative instantaneous quality of the individual user’s
radio channels against their own average channel quali-
ties while the second dimension is related to measuring
the achievable throughput of individual UE’s against the
corresponding average throughput of scheduled users.
Consequently, by understanding the power coefficients s1
Copyright © 2009 SciRes. IJCNS
and s2 as additional adjustable parameters, the exact
scheduler statistics can be tuned and controlled to obtain
a desired balance between the throughput and fairness.
This will be demonstrated in Section 5.
3. Feedback Reporting Process
The overall reporting process between UE’s and BS is
illustrated in Figure 2. Within each time window of
length tr, each mobile sends channel quality indicator
(CQI) reports to BS, formatted and possibly compressed,
with a reporting delay of td seconds [6,8,10,11]. Each
report is naturally subject to errors due to imperfect de-
coding of the received signal. In general, the CQI re-
porting frequency-resolution has a direct impact on the
achievable multi-user frequency diversity and thereon to
the overall system performance and the efficiency of
radio resource management (RRM), as described in gen-
eral e.g. in [11]. In our studies here, the starting point
(reference case) is that the CQI reports are quantized
SINR measurements across the entire bandwidth (wide-
band CQI reporting), to take advantage of the time and
frequency variations of the radio channels for the differ-
ent users. Then also alternative reduced feedback
schemes are described and evaluated, as discussed be-
3.1. Full CQI Reporting
In a general OFDMA radio system, the overall system
bandwidth is assumed to be divided into v CQI meas-
urement blocks. Then quantizing the CQI values to q bits,
the overall full CQI report size is
Sq (8)
bits which is reported by every UE for each TTI [1–3,11].
In case of LTE, with 10 MHz system bandwidth and
grouping 2 physical resource blocks into 1 measurement
Figure 2. Reporting mechanism between UE and BS.
block, it follows that v = 25. Assuming further that quan-
tization is carried with q = 5 bits, then each UE is send
ing 25x5 = 125 bits for every 1ms (TTI length).
3.2. Best-m CQI Reporting
One simple approach to reduce the reporting and feed-
back signalling is obtained as follows. The method is
based on selecting only m < v different CQI measure-
ments and reporting them together with their frequency
positions to the serving cell [8,11]. We assume here that
the evaluation criteria for choosing those m sub-bands
for reporting is based on the highest SINR values (hence
the name best-m). The resulting report size in bits is then
given by
log !( )!
best m
Sqm mv m
 
As an example, with v = 25, q= 5 bits and m = 10, it
follows that Sbest-m = 72 bits, while Sfull = 125 bits. Fur-
thermore, on the scheduler side, we assume that the
PRBs which are not reported by the UE are allocated a
CQI value equal to the lowest reported one.
3.3. Threshold Based CQI Reporting
This reporting scheme is a further simplification and
relies on providing information on only the average CQI
value above certain threshold together with the corre-
sponding location (sub-band index) information. First the
highest CQI value is identified within the full bandwidth,
which sets an upper bound of the used threshold window.
All CQI values within the threshold window are then
averaged and only this information is sent to the BS to-
gether with the corresponding sub-band indexes. On the
scheduler side, the missing CQI values can then be
treated, e.g., as the reported averaged CQI value minus a
given dB offset (e.g. 5 dB, the exact number is again a
design parameter). The number of bits needed for re-
porting is therefore only
Sq=+ (10)
As an example, with v = 25 and q = 5 bits (as above),
it follows that Sthreshold = 30 bits, while Sbest-m = 72 bits
and Sfull = 125 bits. The threshold-based scheme is illus-
trated graphically in Figure 3 [10].
4. System Simulation Model and
In order to evaluate the system-level performance of the
proposed scheduling scheme in a practical OFDMA-based
cellular system context, a comprehensive quasi-static sys-
tem simulator for LTE downlink has been developed,
Copyright © 2009 SciRes. IJCNS
Figure 3. Basic principle of threshold-based CQI reporting.
conforming to the specifications in [1–3]. In the overall
simulation flow, mobile stations are first randomly
dropped or positioned over each sector and cell. Then
based on the individual distances between the mobiles
and the serving base station, the path losses for individ-
ual links are directly determined, while the actual fading
characteristics of the radio channels depend on the as-
sumed mobility and power delay profile. In updating the
fading statistics, the time resolution in our simulator is
set to one TTI (1ms). In general, a standard hexagonal
cellular layout is utilized with altogether 19 cell sites
each having 3 sectors. In the performance evaluations,
statistics are collected only from the central cell site
while the others simply act as sources of inter-cell inter-
As a practical example case, the 10 MHz LTE system
bandwidth mode [1–3] is assumed. The main simulation
parameters and assumptions are generally summarized in
Table 1 for the so-called Macro cell case 1, following
again the LTE working assumptions. As illustrated in
Figure 1, the RRM functionalities are controlled by the
packet scheduler and also link adaptation and HARQ
mechanisms are modelled and implemented, as described
in Table 1. As a practical example, the maximum number
of simultaneously multiplexed users (IBUFF) is set to 10
here. In general, we assume that the BS transmission
power is equally distributed among all PRB’s. In the
basic simulations, 20 UE’s are uniformly dropped within
each sector and experience inter-cell interferences from
the surrounding cells, in addition to path loss and fading.
The UE velocity equals 3km/h, and the typical urban
(TU) channel model standardized by ITU is assumed in
modelling the power-delay spread of the radio channels.
Infinite buffer traffic model is applied in the simulations,
i.e. every user has data to transmit (when scheduled) for
the entire duration of a simulation cycle. The length of a
single simulation run is set to 5 seconds which is then
repeated for 10 times to collect reliable statistics.
In general, every UE has an individual HARQ entry,
Table 1. Basic simulation parameters.
Parameter Assumption
Cellular Layout Hexagonal grid, 19 cell
3 sectors
er site
Inter-site distance 500 m
Carrier Frequency / Bandwidth
Number of active sub-carriers
Sub-carrier spacing
Sub-frame duration
2000 MHz / 10 MHz
15 kHz
0.5 ms
Channel estimation Ideal
PDP ITU Typical Urban 20 paths
Minimum distance between UE
and cell
>= 35 meters
Average number of UE’s per sector 20
Max. number of frequency multi-
lexed UEs
UE receiver type 2-Rx MRC, 2-Rx IRC
Shadowing standard deviation 8 dB
UE speed 3km/h
Total BS TX power (Ptotal) 46dBm
Traffic model Full Buffer
Fast Fading Model Jakes Spectrum
CQI reporting schemes Full CQI
Best-m (with m=10)
Threshold based (with 5dB
CQI log-normal error std. 1 dB
CQI reporting time 5 TTI
CQI delay
CQI quantization
CQI std error
2 TTIs
1 dB
1 dB
MCS rates QPSK (1/3, 1/2, 2/3),
16QAM (1/2, 2/3, 4/5),
64QAM (1/2, 2/3, 4/5)
ACK/NACK delay 2ms
Number of SAW channels 6
Maximum number of retransmisions 3
HARQ model Ideal chase combining (CC)
1st transmission BLER target 20%
Scheduler forgetting factor 0.002
Scheduling schemes used Ordinary PF (for reference)
Modified PF
Simulation duration (one drop) 5 seconds
Number of drops 10
which operates the physical layer re-transmission func-
tionalities. It is based on the stop-and-wait (SAW) pro-
tocol and for simplicity, the number of entries per UE is
fixed to six. HARQ retransmissions are always transmit-
ted with the same MCS and on the same PRB’s (if
scheduled in TD step) as the first transmissions. The
supported modulation schemes are QPSK, 16QAM and
64QAM with variable rates for the encoder as shown in
Table 1.
Link adaptation handles the received UE reports con-
Copyright © 2009 SciRes. IJCNS
Copyright © 2009 SciRes. IJCNS
taining the channel quality information for the whole or
sub-set of PRB’s as described in Section 3. The imple-
mented link adaptation mechanism consists of two sepa-
rate elements – the inner loop (ILLA) and outer loop
(OLLA) LA’s – and are used for removing CQI imper-
fections and estimating supported data rates and MCS.
As a practical example, it is assumed that the CQI report
errors are log-normal distributed with 1dB standard de-
tot ii c
ic H
ictoti ic
where denotes the total noise plus interference
covariance, i.e., .
,tot i
,,tot inoise
s=+Iint i
Using the above modeling and the selected UE re-
ceiver type, the effective SINR values are then calculated
through exponential effective SINR mapping (EESM), as
described in [1–3], for link-to-system level mapping
The actual effective SINR calculations rely on esti-
mated subcarrier-wise channel gains (obtained using
reference symbols in practice) and depend in general also
on the assumed receiver topology. Here we assume the
single-input-multiple-output (SIMO) diversity reception
case, i.e. a single BS transmit antenna and multiple UE
receiver antennas. Considering now an individual UE i,
the SINR per active sub-carrier c at TTI n, denoted here
by ξi,c(n), is calculated according to
5. Results
In this section, we present the system-level performance
results obtained using the previously described quasi-
static radio system simulator. Both ordinary PF and
modified (proposed) PF packet schedulers are used, to-
gether with the three different CQI reporting schemes.
The system-level performance is generally measured and
evaluated in terms of:
,, ,
ic icsigi
ic HH
icnoiseicicinti ic
www ,
Throughput statistics – the cumulative distribution
function (CDF) of the total number of successfully
delivered bits per time unit. Measured at both indi-
vidual UE level as well as overall cell level.
where the time index n is dropped for notational simplic-
ity. Here hic is an NRX x 1 vector of the user i complex
channel gains at subcarrier c from BS to NRX receiver
antennas and wic is the corresponding NRX x 1 spatial fil-
ter used to combine the signals of different receiver an-
tennas (more details below). 2,
ig i
, in turn, denotes the
received nominal signal power per antenna while
and are the covariance matrices of the received
(spatial) noise and interference vectors. The superscript
(.)H denotes conjugate transpose. The noise covariance is
assumed diagonal () and independent of
the user index i. The interference modeling, on the other
hand, takes into account the interference from neighbor-
ing cells. Assuming a total of Lint interference sources,
with corresponding path gain vectors
Coverage – the experienced data rate per UE at the
95% coverage probability (5% UE throughput CDF
,int i
noise =Σ
, the overall
interference covariance at receiving UE i is given by
Jain’s fairness index [19].
In addition to Jain’s index, also the coverage and slope
of the throughput CDF reflect the fairness of the sched-
uling algorithms.
With the proposed modified PF scheduler, different
example values for the power coefficients s1 and s2 are
used as shown in Table 2. To focus mostly on the role of
the channel quality reporting, s2 is fixed here to 1 and the
effects of using different values for s1 are then demon-
strated. This way the impact of the different CQI report-
ing schemes is seen more clearly. For the cases of
Best–m and Threshold based CQI reporting schemes, we
fix the value of m equal to 10 and threshold to 5 dB, re-
spectively. Similar example values have also been used
by other authors in the literature earlier, see e.g. [11].
Complete performance statistics are gathered for both
dual antenna MRC and dual antenna IRC UE receiver
intiintli lic lic
where , denotes the received nominal interferer
power per antenna and per interference source (l).
2,,int l i
Concerning the actual UE receiver topologies (spatial
filters), both maximum ratio combining (MRC) and in-
terference rejection combining (IRC) receivers are de-
ployed in the simulations. These are given by (see, e.g.,
[6] and the references therein)
Table 2. Different power coefficient combinations used to
evaluate the performance of the proposed scheduler.
(13) Coefficient Value
s1 1 2 4 6 8 10 20
s2 1 1 1 1 1 1 1
Figure 4. Left column: Average sector throughput and coverage for different scheduling schemes and assuming dual-antenna
MRC UE receiver type with full CQI feedback (a, b), Best -m CQI feedback (c, d) and Threshold based CQI feedback (e, f).
M1-M7 refer to the modified PF scheduler with power coefficient values as given in Table 2 (M1: s1=1, s2=1, etc.). Right col-
umn: CDF’s of individual UE throughputs for different scheduling schemes and assuming dual-antenna MRC UE receiver
type with full CQI feedback (a), Best -m CQI feedback (b) and Threshold based CQI feedback (c).
5.1. Dual Antenna MRC UE Receiver Case
Figure 4 (left column) illustrates the average sector
throughput and coverage for the different schedulers,
assuming dual antenna maximum ratio combining (MRC)
UE receiver type. The power coefficient values from
Table 2 are presented as index M, where M1 represents
the first couple (s1=1, s2=1), etc, for the metric calcula-
Copyright © 2009 SciRes. IJCNS
tion of the modified PF scheduler. The used reference
scheduler is the ordinary proportional fair approach. In
the first coefficient case (M1), in combination with full
CQI reporting scheme, we achieve coverage gain in the order
of 50% at the expense of only 15% throughput loss as shown
in Figure 4 (a) and (b). This sets the basic reference for com-
parisons in the other cases. In the case of best-m and thresh-
old based reporting schemes presented in and (d), and Figure
4 (e) and (f), we have coverage increases by 57% and 63%
with throughput losses of 16% and 19%, correspondingly.
Figure 5. Left column: MCS distributions [%] for different scheduling principles with (a) Full CQI reporting, (b) Best-m CQI
reporting, and (c) Threshold based CQI reporting assuming dual-antenna MRC UE receiver. Right column: CDF’s of sched-
uled PRB’s per user for different schedulers with (a) Full CQI reporting, (b) Best-m CQI reporting, and (c) Threshold based
CQI reporting assuming dual-antenna MRC UE receiver.
Copyright © 2009 SciRes. IJCNS
Continuing on the evaluation of relative system per-
formance using the modified PF scheduler, we clearly
see a trade-off between average cell throughput and cov-
erage for different power coefficient cases. The remain-
ing power coefficient values shown in Table 2 are used
for tuning the overall system behaviour together with the
choice of the CQI reporting scheme. In the case of full
CQI feedback and coefficient s1 varying between 2 and
10 (M2–M6) the cell throughput loss is decreased to
around 1%, while the coverage gain is reduced to around
6%. Similar behaviour is observed for the other feedback
reporting schemes as well. The exact percentage values
for the coverage gains and throughput losses are stated in
Table 3 in the end.
Further illustrations on the obtainable system per-
formance are presented in Figure 4 (right column) in
terms of the statistics of individual UE data rates for the
applied simulation scenarios. The slope of the CDF re-
flects generally the fairness of the algorithms. Therefore
we aim to achieve steeper slope corresponding to algo-
rithm fairness. This type of slope change behavior can
clearly be established for each simulation scenario.
Clearly, at 5% (coverage) point of the CDF curves, cor-
responding to users typically situated at the cell edges,
we observe significant data rate increases indicated by
shift to the right for all CQI feedback schemes when the
coefficient s1 is changed in the proposed metric. This
indicates improved overall cell coverage at the expense
of slight total throughput loss.
Figure 5 (left column) shows the modulation and cod-
ing scheme (MCS) distributions for different schedulers
and with applied feedback reporting schemes, still as-
suming the case of 2 antenna MRC UE receiver type.
The negligible decrease in higher order modulation usage
(less than 3%) leads to the increase in the lower (more
robust) ones for improving the cell coverage. In all the
simulated cases, the MCS distribution behaviour has a
relatively similar trend following the choice of the power
coefficients in the proposed packet scheduling. In gen-
eral, the use of higher-order modulations is affected
mostly in the most coarse CQI feedback (threshold based)
case while the other two reporting schemes behave fairly
Similarly, Figure 5 (right column) illustrates the
CDF’s of scheduled PRB’s per UE for the different
scheduler scenarios and reporting schemes. Clearly, the
modified PF provides better resource allocation in the
full and best-m feedback cases. Considering the 50%
probability point for the resource allocation, and taking
the case of M1, we have about 5% gain, while in case of
M2 the gain is raised to 15% compared to ordinary PF.
The average obtained improvement for the rest of the
cases is about 33%. In the case of threshold-based feed-
back, the resource allocation is not as efficient, and even
a small reduction in the RB allocation is observed with
small power coefficients, compared to the reference PF
scheduler. Starting from M3, the improvement is anyway
noticeable and the achieved gain is about 20%.
Table 3. Obtained performance statistics compared to ordinary PF scheduler with different CQI reporting schemes and
different power coefficients (M1-M7) for the proposed scheduler. Dual-antenna MRC UE receiver case.
Coverage Gain [%] Throughput Loss [%]
full best-m threshold full best-m threshold
M1 54 57 63 16 16 19
M2 40 42 51 10 10 12
M3 23 26 33 6 6 7
M4 16 18 25 3 4 5
M5 11 14 11 2 3 3
M6 6 7 8 1 2 2
M7 -2 0 -4 0 0 0
Table 4. Obtained performance statistics compared to ordinary PF scheduler with different CQI reporting schemes and
different power coefficients (M1-M7) for the proposed scheduler. Dual-antenna IRC UE receiver case.
Coverage Gain [%] Throughput Loss [%]
full best-m threshold full best-m threshold
M1 56 58 64 15 15 18
M2 43 46 48 9 9 11
M3 26 30 32 6 6 8
M4 17 20 24 4 4 5
M5 10 12 13 2 3 3
M6 8 10 8 2 2 2
M7 -1 1 1 0 1 0
Copyright © 2009 SciRes. IJCNS
5.2. Dual Antenna IRC UE Receiver Case
Next similar performance statistics are obtained for dual
antenna interference rejection combining (IRC) UE re-
ceiver case. Starting from the primary case M1, with full
CQI, we obtain a 13% loss in throughput and 57% cov-
erage improvement. For the reduced feedback reporting
schemes – best-m and threshold based – we have 13%
and 15% throughput losses and 58% and 62% coverage
gains, respectively. Furthermore, resource allocation
gains for full CQI feedback and best-m are 7% for M1
and 17% for M2 correspondingly. The average obtained
improvement for the rest of the cases is about 34%.
Threshold based reporting scheme leads to decrease of
12% for M1 and 7% for M2, and roughly 14% increase
for the rest of simulated cases. The exact percentage
Figure 6. Jain’s fairness index per feedback reporting
scheme for dual-antenna MRC UE receiver case (up) and
dual-antenna IRC UE receiver case (down). Scheduler type
1 means ordinary PF, while 2-8 means proposed modified
PF with power coefficients as described in Table 2.
read from the figures are again stated in table format in
Table 4 in the end.
5.3. Fairness Index
Figure 6 illustrates the Jain’s fairness index per scheduler
for the applied feedback reporting schemes, calculated
over all the ITOT = 20 UE’s using the truly realized UE
throughputs at each TTI and over all the simulation runs.
The value on the x-axis corresponds to the used sched-
uler type, where 1 refers to the reference PF scheduler
and 2-8 refer to the proposed modified PF schedulers
with different power coefficients. The Jain’s fairness
index defined in [19] is generally in the range of [0…1],
where the value of 1 corresponds to all users having the
same amount of resources (maximum fairness). Clearly,
the fairness distribution with the proposed modified PF
scheduler outperforms the used reference PF scheduler
for both UE receiver types. The received fairness gains
are in range of 2%-17% for the MRC receiver case, and
1%-14% for the IRC receiver case, respectively.
6. Conclusions
In this article, we have studied the potential of advanced
packet scheduling principles in OFDMA type radio sys-
tem context, using UTRAN long term evolution (LTE) as
a practical example system scenario. A modified propor-
tional fair scheduler taking both the instantaneous chan-
nel qualities (CQI’s) as well as resource allocation fair-
ness into account was proposed. Also different practical
CQI reporting schemes were discussed, and used in the
system level performance evaluations of the proposed
scheduler. All the performance evaluations were carried
out with a comprehensive quasi-static system level simu-
lator, conforming fully to the current LTE working as-
sumptions. Also different UE receiver types were dem-
onstrated in the performance assessments. In general, the
achieved throughput and coverage gains were assessed
against more traditional ordinary proportional fair sched-
uling. In the case of fixed coverage requirements and
based on the optimal parameter choice for CQI reporting
schemes, the proposed scheduling metric calculations
based on UE channel feedback offers better control over
the ratio between the achievable cell/UE throughput and
coverage increase. As a practical example, even with
limited CQI feedback, the cell coverage can be increased
significantly (more than 30%) by allowing a small de-
crease (in the order of only 5-10%) in the cell throughput.
This is seen to give great flexibility to the overall RRM
process and optimization.
7. Acknowledgments
Fruitful discussions with Markku Kuusela, Nokia De-
vices, Helsinki, Finland, and Dr. Toni Huovinen, Tam-
Copyright © 2009 SciRes. IJCNS
Copyright © 2009 SciRes. IJCNS
pere University of Technology, Tampere, Finland, are
greatly acknowledged.
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